High prevalence of vaccine‐preventable anal human papillomavirus infections is associated with HIV infection among gay, bisexual, and men who have sex with men in Nairobi, Kenya
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is associated with anal cancers and is more prevalent in gay, bisexual, and men who have sex with men (gbMSM), partly due to their vulnerability to HIV infection. Baseline HPV genotype distributions and risk factors can inform the design of next-generation HPV vaccines to prevent anal cancer.A cross-sectional study was conducted among gbMSM receiving care at a HIV/STI clinic in Nairobi, Kenya. Anal swabs were genotyped using a Luminex microsphere array. Multiple logistic regression methods were used to identify risk factors for four HPV outcomes (any HPV, any HR-HPV, and 4- and 9-valent vaccine-preventable HPVs).Among 115 gbMSM, 51 (44.3%) were HIV-infected. Overall HPV prevalence was 51.3%; 84.3% among gbMSM living with HIV and 24.6% among gbMSM without HIV (p < 0.001). One-third (32.2%) had HR-HPV and the most prevalent vaccine-preventable HR-HPV genotypes were 16, 35, 45, and 58. HPV-18 was uncommon (n = 2). The 9-valent Gardasil vaccine would have prevented 61.0% of HPV types observed in this population. In multivariate analyses, HIV status was the only significant risk factor for any HPV (adjusted odds ratio [aOR]:23.0, 95% confidence interval [95% CI]: 7.3-86.0, p < 0.001) and for HR-HPV (aOR: 8.9, 95% CI: 2.8-36.0, p < 0.001). Similar findings were obtained for vaccine-preventable HPVs. Being married to a woman significantly increased the odds of having HR-HPV infections (aOR: 8.1, 95% CI: 1.6-52.0, p = 0.016).GbMSM living with HIV in Kenya are at higher risk of anal HPV infections including genotypes that are preventable with available vaccines. Our findings support the need for a targeted HPV vaccination campaign in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".